Embodiments of the current invention are related to a system and method for monitoring an electrically-connected system having a periodic behavior and for identifying deficiencies in such an electrically-connected system.
In the specification and claims which follow, the expression “electrically-connected” is intended to mean any electrical or mechanical device or system which is a) electrically powered; b) provides/generates electrical power; or c) has characteristics of a combination of being electrically powered and providing electrical power. Exemplary electrically-powered devices/systems include, inter alia, motors and lighting devices. Exemplary electrically-generating devices include, inter alia, generators and turbine systems. Exemplary combination electrically powered and electrically generating systems include, inter alia, hybrid vehicles as known in the art. In the specification and claims which follows, the terms “motor” and “machinery” are intended to mean the electrically powered devices/systems, as noted hereinabove.
Various diagnostic systems have been developed for the early warning of fault detection in electrically-connected rotating machinery/equipment. Many of these diagnostic systems include accelerometers, to collect the mechanical vibrations/waves emitted by the rotating machine, and a control unit to numerically analyze collected waves in the frequency domain. The diagnostic systems typically comprise a sensor or a plurality of sensors and a control unit, as known in the art. The sensors are usually positioned in the vicinity of the rotating parts to be diagnosed, or upon the rotating parts themselves, and the sensors are typically connected electrically to a control unit via cables/wires. As a result, such monitoring/diagnostic systems require a large space within the machinery “envelope”, which represents an additional space burden for the machinery/equipment.
Examples of diagnostic systems are described in PCT patent applications WO 04/017038 and WO 00/04359, whose disclosure are incorporated herein by reference.
Recently, fault detection and diagnosis (FDD) methods have been developed that compare output signals of a complex system with an output signal obtained from a mathematical model of the same fault-free system. The comparison between the signals of the mathematical model and those of the complex system is quantified in terms of one or more “residuals”, i.e. one or more values representing the difference between the two. An analysis of the residual is performed to determine a type of system fault. The analysis includes statistical methods employed to compare the residuals against a database of residuals for systems with known faults.
Until recently it has been difficult to obtain accurate, real-time models for multivariable systems, meaning systems having more than one input and/or one output. If the model of a system is not accurate, the residuals will reflect modeling errors that are very difficult to separate from the effect of actual faults.
Duyar Ahmet et al. in U.S. Pat. No. 6,014,598, whose disclosure is incorporated herein by reference, describe a model based fault detection system and method for monitoring and predicting maintenance requirements of electric motors. The system includes a computer means coupled to sensors which provide continuous real-time information of the input voltage and current and motor speed. The system and method utilize a multivariable experimental modeling algorithm to obtain a mathematical description of the motor. The algorithm compares the modeled result with a measured result and quantifies the comparison in terms of a residual which is generated by subtracting the respective signals. A diagnostic observer analyzes the residual and determines if the motor is fault free or operating in a manner other than fault free. Upon detection of the impending fault, the diagnostic observer evaluates the measured variables of the motor, determines the deviation from the reference value and develops a diagnosis of the likely failed or failing component.
Parlos Alexander et al. in U.S. Pat. No. 6,713,978 and U.S. patent application 2003/0065634, whose disclosure is incorporated herein by reference, describe a non-linear, semi-parametric neural network-based adaptive filter which is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.
Parlos et al., in U.S. Pat. No. 6,590,362 and in US patent application 2003/0067277, whose disclosure is incorporated herein by reference, describe a method and system for early detection of incipient faults in an electric motor. First, current and voltage values for one or more phases of the electric motor are measured during motor operations. A set of current predictions is determined via a neural network-based current predictor based on the measured voltage values and an estimate of motor speed values of the electric motor. A set of residuals is generated by combining the set of current predictions with the measured current values. A set of fault indicators is subsequently computed from the set of residuals and the measured current values. Finally, a determination is made as to whether or not there is an incipient electrical, mechanical, and/or electromechanical fault occurring based on the comparison result of the set of fault indicators and a set of predetermined baseline values.
Additionally, Parlos in U.S. Pat. No. 7,024,335, whose disclosure is incorporated herein by reference, describes assessing the condition of a device includes receiving signals from a sensor that makes electrical measurements of the device. An expected response of the device is estimated in accordance with the received signals, and a measured response of the device is established in accordance with the received signals. An output residual is calculated according to the expected response and the measured response. The condition of the device is assessed by identifying a fault of the device in accordance with the output residual.
There is therefore a need to facilitate simplified and lower cost implementation of monitoring and identifying deficiencies in an electrically-connected system having a periodic behavior.